Abstract: The aim of this article is to show how a tumor can modify energy substrates fluxes in the brain to support its own growth. To address this question we use a modeling approach to explain brain nutrient kinetics. In particular we set up a system of 17 equations for oxygen, lactate, glucose concentrations and cells number in the brain. We prove the existence and uniqueness of nonnegative solutions and give bounds on the solutions. We also provide numerical simulations.PubDate: 2019-03-13

Abstract: Synthetic biology is described as a new field of biotechnology that models itself on engineering sciences. However, this view of synthetic biology as an engineering field has received criticism, and both biologists and philosophers have argued for a more nuanced and heterogeneous understanding of the field. This paper elaborates the heterogeneity of synthetic biology by clarifying the role of design and the variability of design methodologies in synthetic biology. I focus on two prominent design methodologies: rational design and directed evolution. Rational design resembles the design methodology of traditional engineering sciences. However, it is often replaced and complemented by the more biologically-inspired method of directed evolution, which models itself on natural evolution. These two approaches take philosophically different stances to the design of biological systems. Rational design aims to make biological systems more machine-like, whereas directed evolution utilizes variation and emergent features of living systems. I provide an analysis of the methodological basis of these design approaches, and highlight important methodological differences between them. By analyzing the respective benefits and limitations of these approaches, I argue against the engineering-dominated conception of synthetic biology and its “methodological monism”, where the rational design approach is taken as the default design methodology. Alternative design methodologies, like directed evolution, should be considered as complementary, not competitive, to rational design.PubDate: 2019-03-01

Abstract: A new multi-stage deterministic model for the transmission dynamics of syphilis, which incorporates disease transmission by individuals in the early latent stage of syphilis infection and the reversions of early latent syphilis to the primary and secondary stages, is formulated and rigorously analysed. The model is used to assess the population-level impact of preventive (condom use) and therapeutic measures (treatment using antibiotics) against the spread of the disease in a community. It is shown that the disease-free equilibrium of the model is globally-asymptotically stable whenever the associated control reproduction number (denoted by \(\mathcal {R}_T\) ) is less than unity. A special case of the model is shown to have a unique and globally-asymptotically stable endemic equilibrium whenever the associated reproduction number (denoted by \({\tilde{\mathcal {R}}}_T\) ) exceeds unity. Uncertainty and sensitivity analysis of the model, using parameter values and ranges relevant to syphilis transmission dynamics in Nigeria, show that the top three parameters that drive the syphilis infection (with respect to \(\mathcal {R}_T\) ) are the disease transmission rate ( \(\beta\) ), compliance in condom use (c) and efficacy of condom ( \(\epsilon _c\) ). Numerical simulations of the model show that the targeted treatment of secondary syphilis cases is more effective than the targeted treatment of individuals in the primary or early latent stage of syphilis infection.PubDate: 2019-03-01

Abstract: Biologists and philosophers often use the language of determination in order to describe the nature of developmental phenomena. Accounts in terms of determination have often been reductionist. One common idea is that DNA is supposed to play a special explanatory role in developmental explanations, namely, that DNA is a developmental determinant. In this article we try to make sense of determination claims in developmental biology. Adopting a manipulationist approach, we shall first argue that the notion of developmental determinant is causal. We suggest that two different theses concerning developmental determination can be articulated: determination of occurrence and structural determination. We shall argue that, while the first thesis is problematic, the second, opportunely qualified, is feasible. Finally, we shall argue that an analysis of biological causation in terms of determination cannot account for entangled dynamics. Characterising causal entanglement as a particular kind of interactive causation whereby difference-making causes ascribable to different levels of biological organisation influence a particular ontogenetic outcome, we shall, via two illustrative examples, diagnose some potential limits of a reductionist, molecular and intra-level understanding of biological causation.PubDate: 2019-03-01

Abstract: This paper aims to provide a philosophical and theoretical account of biological communication grounded in the notion of organisation. The organisational approach characterises living systems as organised in such a way that they are capable to self-produce and self-maintain while in constant interaction with the environment. To apply this theoretical framework to the study of biological communication, we focus on a specific approach, based on the notion of influence, according to which communication takes place when a signal emitted by a sender triggers a change in the behaviour of the receiver that is functional for the sender itself. We critically analyse the current formulations of this account, that interpret what is functional for the sender in terms of evolutionary adaptations. Specifically, the adoption of this etiological functional framework may lead to the exclusion of several phenomena usually studied as instances of communication, and possibly even of entire fields of investigation such as synthetic biology. As an alternative, we reframe the influence approach in organisational terms, characterising functions in terms of contributions to the current organisation of a biological system. We develop a theoretical account of biological communication in which communicative functions are distinguished from other types of biological functions described by the organisational account (e.g. metabolic, ecological, etc.). The resulting organisational-influence approach allows to carry out causal analyses of current instances of phenomena of communication, without the need to provide etiological explanations. In such a way it makes it possible to understand in terms of communication those phenomena which realise interactive patterns typical of signalling interactions—and are usually studied as such in scientific practice—despite not being the result of evolutionary adaptations. Moreover, this approach provides operational tools to design and study communicative interactions in experimental fields such as synthetic biology.PubDate: 2019-02-02

Abstract: Evolutionary theory coheres with its neighboring theories, such as the theory of plate tectonics, molecular biology, electromagnetic theory, and the germ theory of disease. These neighboring theories were previously unconceived, but they were later conceived, and then they cohered with evolutionary theory. Since evolutionary theory has been strengthened by its several neighboring theories that were previously unconceived, it will be strengthened by infinitely many hitherto unconceived neighboring theories. This argument for evolutionary theory echoes the problem of unconceived alternatives. Ironically, however, the former recommends that we take the realist attitude toward evolutionary theory, while the latter recommends that we take the antirealist attitude toward it.PubDate: 2019-01-24

Abstract: A mathematical model of the dynamics of the immune system is considered to illustrate the effect of its response to HIV infection, i.e. on viral growth and on T-cell dynamics. The specific immune response is measured by the levels of cytotoxic lymphocytes in a human body. The existence and stability analyses are performed for infected steady state and uninfected steady state. In order to keep infection under control, roles of drug therapies are analyzed in the presence of efficient immune response. Numerical simulations are computed and exhibited to illustrate the support of the immune system to drug therapies, so as to ensure the decay of infection and to maintain the level of healthy cells.PubDate: 2018-12-04

Abstract: Busseola fusca is a maize and sorghum pest that can cause significant damage to both crops. Given that maize is one of the main cereals grown in the worldwide, this pest is a major challenge for maize production and therefore for the economies of several countries . In this paper , based on the life cycle of B. fusca, we propose a mathematical model to study the population dynamics of this insect pest . A sensitivity analysis using the eFast method was performed to show the most important parameters of the model. We present the theoretical analysis of the model. More precisely, we derive a threshold parameter \({\mathcal {N}}_0\) , called basic offspring number and show that the trivial equilibrium is globally asymptotically stable whenever \({\mathcal {N}}_0\le 1\) , while if \({\mathcal {N}}_0>1\) , the non trivial equilibrium is globally asymptotically stable. The theoretical results are supported by numerical simulations.PubDate: 2018-12-01

Authors:M. A. Aziz-Alaoui; M. Daher Okiye; A. MoussaouiAbstract: The main concern of this paper is to study the dynamic of a predator–prey system with diffusion. It incorporates the Holling-type-II and a modified Leslie–Gower functional responses under Robin boundary conditions. More concretely, we study the dissipativeness of the system by using the comparison principle, and we derive a criteria for permanence and for predator extinction.PubDate: 2018-05-28DOI: 10.1007/s10441-018-9332-0

Authors:G. N. Zholtkevych; K. V. Nosov; Yu. G. Bespalov; L. I. Rak; M. Abhishek; E. V. VysotskayaAbstract: The state-of-art research in the field of life’s organization confronts the need to investigate a number of interacting components, their properties and conditions of sustainable behaviour within a natural system. In biology, ecology and life sciences, the performance of such stable system is usually related to homeostasis, a property of the system to actively regulate its state within a certain allowable limits. In our previous work, we proposed a deterministic model for systems’ homeostasis. The model was based on dynamical system’s theory and pairwise relationships of competition, amensalism and antagonism taken from theoretical biology and ecology. However, the present paper proposes a different dimension to our previous results based on the same model. In this paper, we introduce the influence of inter-component relationships in a system, wherein the impact is characterized by direction (neutral, positive, or negative) as well as its (absolute) value, or strength. This makes the model stochastic which, in our opinion, is more consistent with real-world elements affected by various random factors. The case study includes two examples from areas of hydrobiology and medicine. The models acquired for these cases enabled us to propose a convincing explanation for corresponding phenomena identified by different types of natural systems.PubDate: 2018-05-24DOI: 10.1007/s10441-018-9321-3

Authors:M. Susree; M. AnandAbstract: This computational study generates a hypothesis for the coagulation protein whose initial concentration greatly influences the course of coagulation. Many clinical malignancies of blood coagulation arise due to abnormal initial concentrations of coagulation factors. Sensitivity analysis of mechanistic models of blood coagulation is a convenient method to assess the effect of such abnormalities. Accordingly, the study presents sensitivity analysis, with respect to initial concentrations, of a recently developed mechanistic model of blood coagulation. Both the model and parameters to which model sensitivity is being analyzed provide newer insights into blood coagulation: the model incorporates distinct equations for plasma-phase and platelet membrane-bound species, and sensitivity to initial concentrations is a new dimension in sensitivity analysis. The results show that model predictions are most uncertain with respect to changes in initial concentration of factor VIII, and this hypothesis is supported by results from other models developed independently.PubDate: 2018-05-14DOI: 10.1007/s10441-018-9329-8

Authors:Angélique Stéphanou; Eric Fanchon; Pasquale F. Innominato; Annabelle BallestaAbstract: Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.PubDate: 2018-05-09DOI: 10.1007/s10441-018-9330-2

Authors:G. Kolaye; I. Damakoa; S. Bowong; R. Houe; D. BékollèAbstract: A mathematical model for Vibrio Cholerae (V. Cholerae) in a closed environment is considered, with the aim of investigating the impact of climatic factors which exerts a direct influence on the bacterial metabolism and on the bacterial reservoir capacity. We first propose a V. Cholerae mathematical model in a closed environment. A sensitivity analysis using the eFast method was performed to show the most important parameters of the model. After, we extend this V. cholerae model by taking account climatic factors that influence the bacterial reservoir capacity. We present the theoretical analysis of the model. More precisely, we compute equilibria and study their stabilities. The stability of equilibria was investigated using the theory of periodic cooperative systems with a concave nonlinearity. Theoretical results are supported by numerical simulations which further suggest the necessity to implement sanitation campaigns of aquatic environments by using suitable products against the bacteria during the periods of growth of aquatic reservoirs.PubDate: 2018-05-04DOI: 10.1007/s10441-018-9322-2

Authors:Nikolai Bessonov; Natalia Reinberg; Malay Banerjee; Vitaly VolpertAbstract: Darwin described biological species as groups of morphologically similar individuals. These groups of individuals can split into several subgroups due to natural selection, resulting in the emergence of new species. Some species can stay stable without the appearance of a new species, some others can disappear or evolve. Some of these evolutionary patterns were described in our previous works independently of each other. In this work we have developed a single model which allows us to reproduce the principal patterns in Darwin’s diagram. Some more complex evolutionary patterns are also observed. The relation between Darwin’s definition of species, stated above, and Mayr’s definition of species (group of individuals that can reproduce) is also discussed.PubDate: 2018-04-30DOI: 10.1007/s10441-018-9328-9

Authors:Duc-Hau Le; Doanh Nguyen-NgocAbstract: Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.PubDate: 2018-04-26DOI: 10.1007/s10441-018-9325-z

Authors:Moitri Sen; Ashutosh Simha; Soumyendu RahaAbstract: This paper deals with designing a harvesting control strategy for a predator–prey dynamical system, with parametric uncertainties and exogenous disturbances. A feedback control law for the harvesting rate of the predator is formulated such that the population dynamics is asymptotically stabilized at a positive operating point, while maintaining a positive, steady state harvesting rate. The hierarchical block strict feedback structure of the dynamics is exploited in designing a backstepping control law, based on Lyapunov theory. In order to account for unknown parameters, an adaptive control strategy has been proposed in which the control law depends on an adaptive variable which tracks the unknown parameter. Further, a switching component has been incorporated to robustify the control performance against bounded disturbances. Proofs have been provided to show that the proposed adaptive control strategy ensures asymptotic stability of the dynamics at a desired operating point, as well as exact parameter learning in the disturbance-free case and learning with bounded error in the disturbance prone case. The dynamics, with uncertainty in the death rate of the predator, subjected to a bounded disturbance has been simulated with the proposed control strategy.PubDate: 2018-04-23DOI: 10.1007/s10441-018-9323-1

Authors:Ilhem Bouderbala; Nadjia El Saadi; Alassane Bah; Pierre AugerAbstract: In this paper, we develop a 3D-individual-based model (IBM) to understand effect of various small-scale mechanisms in phytoplankton cells, on the cellular aggregation process. These mechanisms are: spatial interactions between cells due to their chemosensory abilities (chemotaxis), a molecular diffusion and a demographical process. The latter is considered as a branching process with a density-dependent death rate to take into account the local competition on resources. We implement the IBM and simulate various scenarios under real parameter values for phytoplankton cells. To quantify the effects of the different processes quoted above on the spatial and temporal distribution of phytoplankton, we used two spatial statistics: the Clark–Evans index and the group belonging percentage. Our simulation study highlights the role of the branching process with a weak-to-medium competition in reinforcing the aggregating structure that forms from attraction mechanisms (under suitable conditions for diffusion and attraction forces), and shows by contrast that aggregations cannot form when competition is high.PubDate: 2018-03-15DOI: 10.1007/s10441-018-9318-y